Water and wetland are components of natural resources that play an indispensable role in the ecological environment and water circulation within drainage basins. Using GFDM-1 satellite images with a spatial resolution higher than 0.5m, we performed a water and wetland monitoring application research in Ganzhou, located in Gansu Province in northwestern China. The findings indicate that with high resolution and nine spectral bands, GFDM-1 images provide significant advantages in achieving high quality fine classification. Specifically, it achieved a total classification accuracy of 92.38% for water and 86.19% for wetlands within the demonstration area. In general, the GFDM-1 satellite remote sensing images have good classification and target recognition capabilities, demonstrating significant potential for application in water and wetland monitoring.

In order to give full play to the application efficiency of land remote sensing satellites for natural resources and strengthen the protection of cultivated land in China, this paper introduces a two-stage intelligent extraction strategy for erosion gullies. This strategy integrates deep learning object detection with interactive semantic segmentation.Initially, an iterative optimization method of object detection samples and models based on incremental learning and confidence filtering is proposed, achieving automatic identification of erosion gullies. Then, an interactive semantic segmentation method based on edge constraint is adopted to extract the precise contours of erosion gullies semi-automatically. An erosion gully extraction experiment was conducted in Heilongjiang Province of China, utilizing satellite images with a spatial resolution of 2 meters. Accuracy assessment was performed using GF-7 satellite imagery and verified through field verification. The results indicate that the Precision of erosion gully extraction in test area was 95.4%, the Recall was 93.0%, and the F1_score was 94.2%. The feasibility and accuracy of the proposed method has been verified by the experiments.

Pixel-level segmentation with deep learning is widely applied in interpretation tasks of high-resolution remote sensing imagery, such as for change detection and object extraction. However, most existing methods focus on designing deep learning model structures, but few consider improving segmentation accuracy in the inference stage with a trained model. The most important advantage of improving model accuracy in the inference stage is that it doesn’t need to re-train models, but can improve the detection accuracy by a cost-effective means. In this letter, a novel decision-level fusion method based on the Dempster-Shafer theory (DS) was proposed, namely DeepDSFusion. As a general method, it can be seamlessly integrated into any other pixel-level segmentation model. In the implementation detail of the DeepDSFusion, firstly, several classical data augmentation methods, such as rotation transform and scale transform, were adopted to acquire multiscale probability maps. Then, DS theory was used to fuse multiscale probability maps into a single probability map. Finally, a simple threshold is applied in the single probability map to acquire segmentation results. Three classical pixel-level segmentation tasks, deforestation detection, road extraction, and landcover mapping on high-resolution imagery prove the effectiveness of DeepDSFusion.

As an important part of the renewable energy, photovoltaic power generation industry has developed rapidly all around China in recent years, however some land use problems have also emerged. Therefore it is of great significance to monitor the number and distribution of photovoltaic power stations timely and accurately with high-resolution satellite images for the healthy development of photovoltaic industry. Combined with the improved DeepLab V3+ model and the ResNeSt-50 backbone network, the paper designs an effective photovoltaics extraction semantic segmentation algorithm and trains the new extraction model iteratively by making full use of big and various photovoltaic land samples. Photovoltaics are extracted accurately all over China with Chinese high-resolution satellite images, following a series of post-processing algorithms, such as binarizing, small and pseudo targets automatic removing, etc. Results show that the accuracy rate of photovoltaic land extraction is about 72.36% and the recall rate is about 91.06%. This precision is good enough for photovoltaic land extraction nationwide annually and the proposed deep learning model is efficient, small and can widely be used with other natural resources target extraction.

Surface water is an irreplaceable strategic resource for human survival and social development. It is of great significance to fully grasp the quantity, spatial distribution and dynamic changes of surface water in China accurately, quickly and timely. With the rapid development of Chinese remote sensing satellite industry, monitoring surface water in whole China quarterly has become a task of challenge but achievable. This paper analyzes and compares the advantages and disadvantages of existing algorithms for surface water extraction. And a practical and useful algorithm workflow is proposed. Based on 2-meter resolution multi-spectral satellite images, the paper adopts the regional fast-growing water extraction algorithm combined with water element buffers. 1,265 above level 3 rivers, their associated 2,128 reservoirs and 2,953 above-1-km2 natural lakes in China have been fast extracted quarterly with high-precision. Practice has shown that the automatic extraction algorithm is one of the most effective algorithms for realizing the automatic monitoring of large-scale surface water.

Based on high resolution satellite images (ZY3, GF1) and Landsat MSS/TM/ETM+ data, the spatial and temporal variation of mangroves in Jiulong River estuary, Fujian from 1978 to 2018 were extracted by combining spectral prior knowledge and visual interpretation. The results showed that the area of mangrove forests has experienced three stages, respectively, slow increase (1980-1990), rapid expansion (1990-2013) and the rate of growth slowdown (2013-2018). In 1978, the area of mangrove resources was only 98.09 hectares, and it increased to 404.85 hectares in 2018. The constant combination of artificial planting and natural recovery was the main reason for maintaining the steady growth of mangrove areas. Meanwhile, land use policies, human activities, and ecological factors also exerted influence on the variation of mangrove in Jiulong River estuary.

Mangroves provide a variety of irreplaceable functions such as coastline protection, bay improvement, water purification and wetland diversity protection. However, mangroves are fragile ecosystems and are affected by human activities and climate change. Long-term monitoring on mangroves is of great significance. This paper selected Tongming Bay located in southern China coast as a study case because of its high variation of mangrove extent. Satellite remote sensing images from 1978 to 2018 were used for mangrove extent interpretation, and to obtain the distribution, changes, and main driving factors of changes in the Tongming Bay area in the past 40 years. The results showed that the area of mangrove extent in Tongming Bay continued to shrink from 1978 to 2013, and the area gradually increased after 2013. The main driving factor for the reduction of mangroves was the artificial aquaculture occupation of mangrove habitats, and mangrove area growth was mainly caused by artificial planting. The results of this study can provide references for local government on mangrove management and ecological restoration.

In the disaster research field, extraction of post-disaster damaged house building information plays a critical role in post-disaster emergency rescue and disaster-induced damage assessment. In this study, we propose a method of automatically extracting house damage information from post-quake high-resolution optical remote-sensing imagery through multiscale fusion of spectral texture features. This is achieved in three steps. First, the texture features and spectral features of images are enhanced at the pixel level; then, the resulted feature images are fused at the feature level and the fused feature images are superpixel-segmented; finally, a post-quake house damage index model is constructed. The results show an overall accuracy of 76.75%, 75.35%, and 83.25% for the three different types of imagery studied. This suggests that our algorithm is applicable to extracting damage information from multisource remote sensing data and can provide useful guidance for post-disaster rescue and assessment based on regional house damage conditions.

The extraction of spatial details is crucial for fusion quality. An efficient way is to exploit the difference between high-resolution panchromatic (Pan) images and low-resolution Pan (LRP), which is to be simulated by weighted average value from low-resolution multispectral images. To obtain the weighting coefficients with multivariate linear regression, three issues were discussed, and corresponding solutions were proposed in this letter. The proposed method consists of separating high-frequency pixels from low-frequency pixels using support vector machine and selecting observations that are evenly distributed by a bucketing technique and forcing coefficients to be sound physically by constrained least squares. Validation experiments are undertaken using three IKONOS data sets, and fusion results are compared against four popular methods. The results show that the proposed method can simulate LRP soundly and therefore achieve a better fusion quality.